This repository implements the CVPR 2019 paper on class-balanced loss based on effective number of samples, designed to address class imbalance in deep learning training. The code provides loss functions and training scripts for CIFAR datasets (both original and long-tailed variants), along with focal loss implementation. It targets fine-grained visual categorization problems using TensorFlow, supporting datasets like ImageNet and iNaturalist.